Wind Speed Prediction Using SVM in MATLAB

In this article, I present a wind speed prediction implementation using Support Vector Machine (SVM) in MATLAB. The code was developed from scratch to demonstrate how SVM regression (SVR) can effectively forecast wind speeds based on historical data and relevant meteorological factors. The implementation utilizes MATLAB's Statistics and Machine Learning Toolbox, specifically the fitrsvm function for regression tasks, which allows flexible configuration of kernel functions (e.g., linear, polynomial, or RBF) and hyperparameters like box constraint and epsilon-sensitive loss. Key implementation aspects include: - Feature selection from time-series wind data including temporal patterns and meteorological covariates - Data normalization using z-score standardization to improve SVM convergence - Kernel function optimization through cross-validation to handle nonlinear relationships - Model evaluation metrics such as RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) The SVM approach proves particularly effective for wind prediction due to its capability to capture complex nonlinear patterns while maintaining generalization performance. I welcome technical discussions on feature engineering techniques, alternative kernel selections, and comparative analyses with other machine learning approaches like ANN or ARIMA models. Suggestions for optimizing computational efficiency in large-scale time-series applications are especially appreciated.